Robust medical zero‐watermarking algorithm based on Residual‐DenseNet
Abstract To solve the problem of poor robustness of existing traditional DCT‐based medical image watermarking algorithms under geometric attacks, a novel deep learning‐based robust zero‐watermarking algorithm for medical images is proposed. A Residual‐DenseNet is designed, which took low‐frequency f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-11-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12100 |
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| _version_ | 1832546692012965888 |
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| author | Cheng Gong Jing Liu Ming Gong Jingbing Li Uzair Aslam Bhatti Jixin Ma |
| author_facet | Cheng Gong Jing Liu Ming Gong Jingbing Li Uzair Aslam Bhatti Jixin Ma |
| author_sort | Cheng Gong |
| collection | DOAJ |
| description | Abstract To solve the problem of poor robustness of existing traditional DCT‐based medical image watermarking algorithms under geometric attacks, a novel deep learning‐based robust zero‐watermarking algorithm for medical images is proposed. A Residual‐DenseNet is designed, which took low‐frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high‐level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks. |
| format | Article |
| id | doaj-art-b9cea3330a31473990943e74078cbed2 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-b9cea3330a31473990943e74078cbed22025-02-03T06:47:35ZengWileyIET Biometrics2047-49382047-49462022-11-0111654755610.1049/bme2.12100Robust medical zero‐watermarking algorithm based on Residual‐DenseNetCheng Gong0Jing Liu1Ming Gong2Jingbing Li3Uzair Aslam Bhatti4Jixin Ma5School of Information and Communication Engineering Hainan University Haikou ChinaResearch Center for Healthcare Data Science Zhejiang Lab Hangzhou ChinaSchool of Traffic and Transportation Nanchang JiaoTong Institute Nanchang ChinaSchool of Information and Communication Engineering Hainan University Haikou ChinaSchool of Information and Communication Engineering Hainan University Haikou ChinaSchool of Computing & Mathematical Sciences University of Greenwich London UKAbstract To solve the problem of poor robustness of existing traditional DCT‐based medical image watermarking algorithms under geometric attacks, a novel deep learning‐based robust zero‐watermarking algorithm for medical images is proposed. A Residual‐DenseNet is designed, which took low‐frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high‐level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.https://doi.org/10.1049/bme2.12100deep learningmedical imagerobustnesszero‐watermarking |
| spellingShingle | Cheng Gong Jing Liu Ming Gong Jingbing Li Uzair Aslam Bhatti Jixin Ma Robust medical zero‐watermarking algorithm based on Residual‐DenseNet IET Biometrics deep learning medical image robustness zero‐watermarking |
| title | Robust medical zero‐watermarking algorithm based on Residual‐DenseNet |
| title_full | Robust medical zero‐watermarking algorithm based on Residual‐DenseNet |
| title_fullStr | Robust medical zero‐watermarking algorithm based on Residual‐DenseNet |
| title_full_unstemmed | Robust medical zero‐watermarking algorithm based on Residual‐DenseNet |
| title_short | Robust medical zero‐watermarking algorithm based on Residual‐DenseNet |
| title_sort | robust medical zero watermarking algorithm based on residual densenet |
| topic | deep learning medical image robustness zero‐watermarking |
| url | https://doi.org/10.1049/bme2.12100 |
| work_keys_str_mv | AT chenggong robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet AT jingliu robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet AT minggong robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet AT jingbingli robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet AT uzairaslambhatti robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet AT jixinma robustmedicalzerowatermarkingalgorithmbasedonresidualdensenet |